Evaluation of satellite retrievals of liquid clouds from the GOES-13 imager and MODIS over the midlatitude North Atlantic during the NAAMES campaign

David Painemal,Douglas Spangenberg,Claire Robinson,Kenneth L Thornhill,Brian Cairns,Luke Ziemba,Edward L Winstead,Patrick Minnis,Ewan Crosbie,Richard H Moore,William L Smith Jr

doi:10.5194/amt-14-6633-2021

Abstract

Abstract. Satellite retrievals of cloud droplet effective radius (re) and optical depth (τ) from the Thirteenth Geostationary Operational Environmental Satellite (GOES-13) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua and Terra, based on the Clouds and the Earth's Radiant Energy System (CERES) project algorithms, are evaluated with airborne data collected over the midlatitude boundary layer during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). The airborne dataset comprises in situ re from the Cloud Droplet Probe (CDP) and remotely sensed re and τ from the airborne Research Scanning Polarimeter (RSP). GOES-13 and MODIS (Aqua and Terra) re values are systematically greater than those from the CDP and RSP by at least 4.8 (GOES-13) and 1.7 µm (MODIS) despite relatively high linear correlation coefficients (r=0.52–0.68). In contrast, the satellite τ underestimates its RSP counterpart by −3.0, with r=0.76–0.77. Overall, MODIS yields better agreement with airborne data than GOES-13, with biases consistent with those reported for subtropical stratocumulus clouds. While the negative bias in satellite τ is mostly due to the retrievals having been collected in highly heterogeneous cloud scenes, the causes for the positive bias in satellite re, especially for GOES-13, are more complex. Although the high viewing zenith angle (∼65∘) and coarser pixel resolution for GOES-13 could explain a re bias of at least 0.7 µm, the higher GOES-13 re bias relative to that from MODIS is likely rooted in other factors. In this regard, a near-monotonic increase was also observed in GOES-13 re up to 1.0 µm with the satellite scattering angle (Θ) over the angular range 116–165∘; that is, re increases toward the backscattering direction. Understanding the variations of re with Θ will require the combined use of theoretical computations along with intercomparisons of satellite retrievals derived from sensors with dissimilar viewing geometry.

Highlights

Cloud properties estimated from satellite passive sensors have been crucial in advancing our knowledge of the role of clouds in the climate system and the Earth’s energy budget (e.g., Loeb et al, 2009; Kato et al, 2011)
Given the normalization applied to the data, uncertainties in the Cloud Droplet Probe (CDP) should have a negligible impact in the result interpretation
Two key aspects that emerge from the normalized profiles are the following: (a) re is a maximum near the cloud top, and (b) a vertically stratified cloud model is expected to fit the observations reasonably well, implying that liquid water path (LWP) can be more precisely estimated by LWP = 5/9 · ρ · re · τ, as opposed to the vertically homogeneous equation LWP = 2/3·ρ ·re ·τ, as suggested by studies in the subtropics (e.g., Seethala and Horvath, 2010; Painemal et al, 2017)

Summary

Introduction

Cloud properties estimated from satellite passive sensors have been crucial in advancing our knowledge of the role of clouds in the climate system and the Earth’s energy budget (e.g., Loeb et al, 2009; Kato et al, 2011). D. Painemal et al.: MODIS and GOES-13 cloud retrieval validation over midlatitudes during NAAMES groups around the world to detect clouds and derive cloud phase, effective radius, optical depth, liquid/ice water path, and height (Stubenrauch et al, 2013; Roebeling et al, 2015). Painemal et al.: MODIS and GOES-13 cloud retrieval validation over midlatitudes during NAAMES groups around the world to detect clouds and derive cloud phase, effective radius, optical depth, liquid/ice water path, and height (Stubenrauch et al, 2013; Roebeling et al, 2015) These geostationary cloud properties are receiving increased attention as their high temporal resolution allows for continuous monitoring of cloud systems, making the datasets ideal for numerous weather applications, including nowcasting and data assimilation (e.g., Benjamin et al, 2016; Jones et al, 2018)

Results

Discussion

Conclusion